基于车载视频图像的道路消失点检测算法

道路消失点检测是高级驾驶辅助系统中盲区监测的重要组成部分。针对现有消失点检测方法所存在的准确度低、运算量大等问题,提出一种基于车载视频图像的道路消失点检测算法。该算法在Harris角点检测基础上优化得分函数检测出图像特征点,减少在跟踪阶段的运算量；通过金字塔光流法和帧差距离对运动特征点进行跟踪,在结束帧上准确获得各特征点的位置；对特征点去除离值点后,通过优化初始聚类中心的K-Means聚类算法,得到车载视频图像的道路消失点。最后将算法应用于各种车辆行驶场景进行测试,在较短运行时间内,能准确检测出车载视频图像中道路消失点,证明算法鲁棒性好、运算简单易实现。

Road Vanishing Point Detection Algorithm Based On In-vehicle Video Images

Road vanishing point detection is an important part of blind zone monitoring in advanced driver assistance systems. In view of the problems of low accuracy and large computation of existing vanishing point detection methods, a road vanishing point detection algorithm based on in-vehicle video images is proposed. The algorithm detected the image feature points based on Harris corner point detection by optimizing the score function to reduce the amount of operations in the tracking stage. It tracked the motion feature points by the pyramid optical flow method and frame difference distance, and accurately obtained the position of each feature point on the end frame. After removing the outlying points from the feature points, the K-Means clustering algorithm that optimized the initial clustering center was used to obtain the road vanishing point of the in-vehicle video image. Finally, the algorithm was applied to various vehicle driving scenes for testing. It can accurately detect the road vanishing points in the in-vehicle video images within a short running time, which proves that the algorithm is robust, simple and easy to implement.